Exploration as learned strategy

Hello all 🙂

I am currently working on a RL algorithm using GNNs to optimize a network of data centers with dynamically changing client locations. However, one caveat is that the agent has very little information at the start about the network (only latencies between initial configuration of data centers). He can relocate a passive node which costs not much to retrieve information of potential other locations. This has no effect on the overall latency, which is determined by the active data centers. He also can relocate active nodes, however, this is costly.

So, the agent has to learn a strategy where he explores always at the beginning (at the very start, this will probably be even random) and as he collects more information about the network, he can start to relocate the active nodes.

The question now is, if you know of any papers that incorporate similar strategies where the agent should learn an exploration strategy which is then also used for inference on the live system and not only for training (where exploration is of course very essential and occurs in most training algorithms). Or if you have any experience, I would be glad to hear your opinions on that topic.

Best regards and thank you!

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